A Novel Method for Wind Power Abnormal Data Identification and Cleaning Based on Boundary Modeling

Various factors such as communication interference or failures, maintenance or protection shutdowns of wind turbines, and downrating under grid dispatch control contribute to the presence of significant abnormal data in the actual operating data collected from wind turbines. In this article, a method is proposed to identify abnormal data in wind power using boundary modeling. The abnormal data are classified into three types based on distribution characteristics. A stepwise procedure is presented for identifying abnormal data. The bottom-stacked abnormal data are initially identified using rule-based filtering. The scattered abnormal data are preliminarily identified through region partition using clustering, combined with the quartile method. Datasets comprising wind speed range and mean wind speed at various wind power levels are acquired. A boundary model is developed by analyzing the correlation between wind power and respective wind speed ranges, as well as the correlation between power and corresponding mean wind speeds, to refine the identification of middle-stacked and scattered abnormal data. Finally, the effectiveness and universality of the proposed method are verified using both actual data and simulated data. Quantitative analysis is performed using wind power curve modeling to evaluate the accuracy of the abnormal data identification results.